Increasing participation of underrepresented groups in cancer early detection research: a scoping review
Notice bibliographique
Résumé
Background: Improvements in access to early-detection research on cancer are still urgently needed to ensure that new research on early-stage cancer detection benefits all groups in society. To achieve this, cancer early detection (ED) studies must include participants from all walks of life. There are unique aspects to cancer early detection research that may deter potential research participants and complicate efforts to involve people from underrepresented backgrounds that require a review on its own merit. For instance, a unique risk for cancer ED research is overdiagnosis and overtreatment, in which a tumor is uncovered and treated that would not have led to the patient's death if left undiscovered and untreated. This potential 'side effect' of cancer ED research participation is particularly problematic for those without adequate access to healthcare and insurance. Methods: We conducted a targeted scoping review to identify empirically tested approaches to improve participation of underrepresented groups in cancer early detection research. Searches were conducted in PubMed and PsycINFO using terms related to cancer, research participation, and underserved populations in the title and/or abstract. Eligible studies were peer-reviewed, published between 2002 and April 2022, conducted in high-income countries, focused on adults without a cancer diagnosis, and reported on their evaluation of an intervention designed to improve recruitment or participation of minoritized groups in cancer early detection research. Data were extracted on study characteristics, barriers to participation, intervention strategies, and outcomes of the recruitment and engagement intervention that was assessed. We analyzed data extracted using narrative synthesis to identify cross-cutting themes across barriers to participating, and recruitment or engagement approaches. Results: This review identified themes in the 38 included studies that aimed to recruit and involve participants from underserved groups in cancer ED research so that future studies may learn from or further test these varied strategies. We narratively grouped the review in terms of the barriers identified, and the approaches that have been designed to improve participation. These included rethinking recruitment locations and partnerships with local communities, designing educational interventions, combining research with community needs, increasing cultural competence of research teams, and overcoming practical barriers in study design. Conclusion: This scoping literature review highlights various tools, empirically tested, that research teams can employ to improve participation rates of groups underrepresented in cancer ED research. Combinations of these methods could help overcome the perceived barriers to participation in cancer research that mainly affect people without a cancer diagnosis from these minoritized groups. Not only would these methods increase the generalizability and representativeness of studies; the highlighted approaches also contribute to a more significant shift in research culture toward less extractive and more trusting relationships between researchers and the public.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,025 | 0,056 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,007 | 0,005 |
| Bibliométrie | 0,010 | 0,010 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,006 | 0,005 |
| Science ouverte | 0,002 | 0,004 |
| Intégrité de la recherche | 0,005 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».